Shared vehicle deployment and reallocation using predicted and current demand location and transit data

ABSTRACT

A vehicle allocation system oversees the operation of a plurality of auto-piloted vehicles, used to transport passengers between major ports such as airports, train stations, shopping malls, etc. The vehicle allocation system establishes a search time interval and predicts passenger demands and the status of the monitored vehicles within the search time interval. A predetermined number of vehicles are assigned to each of the ports in an area. Each port includes a parking lot or queue to accommodate waiting vehicles and a terminal which notifies a host computer of the number of currently available vehicles, passenger demands for vehicles, vehicle destination information, arrival information and so forth. The host computer has a memory storing predicted demand data based upon past demand history. The host computer calculates any excess or deficiency of vehicles in the ports on the basis of the predicted demand data and the information obtained from the terminals within the search time interval. On the basis of the result of such calculation, vehicles are reallocated from ports having an excess of vehicles to ports lacking sufficient vehicles.

This application claims benefit of Provisional Nos. 60/089,506 filedJun. 16, 1998 and 60/092,964 filed Jul. 15, 1998.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to a vehicle distribution systemand, more particularly, to a vehicle distribution system for providing astable supply or distribution of vehicles to a plurality of ports withinan area so as to keep up with ride demands at each of the ports.

2. Description of the Relevant Art

Where vehicles need to be distributed among a plurality of ports withinan area in response to ride demands generated at each of the ports, someports may lack vehicles to meet their ride demands while others may haveexcess vehicles. In such a case, a vehicle distribution system may bedevised to reallocate or redistribute surplus vehicles between ports toreplenish shortages of vehicles at other ports.

According to the background art, one vehicle distribution system isdesigned to deal with vehicle shortages on a posterior basis. In otherwords, vehicles are reallocated when a shortage occurs and not inanticipation of a predicted shortage. It takes time to move surplusvehicles from one port to another. Therefore, if a new ride demandoccurs while vehicle redistribution is under way, or if some vehiclesleft ports on their way to other ports before a redistribution processis initiated, a surplus or shortage of vehicles may occur again at anyport upon completion of the redistribution. Such occurrences can causepassengers to be left wait on vehicles for extended periods of time,such that a preferred minimum waiting time for passengers cannot beobserved in the face of varying ride demands.

One solution to the above deficiency may be for vehicles to beredistributed on the basis of predicted ride demand data.Illustratively, a system may be devised to distribute vehicles accordingto predicted ride demand data based on the number of existing vehiclesat each of the ports, on the ride demands currently generated at theport in question, and on past statistical ride demand data regarding theport.

One example of that system is a vehicle demand predicting systemdisclosed in Laid-Open Japanese Patent Application No. Hei 9-153098.This vehicle demand predicting system utilizes the statistical data asraw data. This means that if the raw data differ from actual ridedemands, the system is significantly affected by such variances. Theresult can be a vehicle distribution system of poor accuracy, with alarge number of vehicles redistributed unproductively (as will bediscussed in detail with reference to FIGS. 14 and 15). If the systemredistributes vehicles while predicting ride demands using statisticaldata, the redistribution process should preferably be carried out with aminimum of wastefully redistributed vehicles, even if actual ridedemands deviate from the statistical data.

SUMMARY OF THE INVENTION

It is therefore an object of the present invention to provide a vehicledistribution system capable of minimizing wastefully redistributedvehicles in the face of actual ride demand fluctuations.

These and other objects of the invention are fulfilled by providing amethod of operating a vehicle allocation system for allocating a firstnumber of vehicles amongst a second number of vehicle ports wherepassengers demand the services of one or more vehicles, said methodcomprising the steps of: acquiring past vehicle demand data based uponpast passenger transportation activity; forming predictive vehicledemand data, based upon the past vehicle demand data; storing thepredictive vehicle demand data; detecting a number of available vehiclesat each of the ports; sensing current vehicle demand data representingpassengers actually seeking transportation at each of the ports; readingvehicle destination and arrival data of vehicles starting or in transitto one of the ports; predicting the number of arriving vehicles at eachof the ports based upon the vehicle destination and arrival data;determining whether a given port has a deficiency or an excess ofvehicles by analyzing the current vehicle demand data, the predictivevehicle demand data, the number of available vehicles, and the number ofarriving vehicles, for a predetermined period of time; and reallocatingvehicles from a port determined to have an excess of vehicles to a portdetermined to have a deficiency of vehicles.

These and other objects of the invention are also fulfilled by providinga method of operating a vehicle redistribution system for predictingride demands based on ride demands generated at a plurality of ports inan area, as well as, based upon statistical ride demand data theplurality of ports in order to redistribute vehicles from a port havingsurplus vehicles to a port lacking vehicles, said method comprising thesteps of: predicting total ride demand data on a daily basis from thestatistical ride demand data; selecting a number of total vehicles to bedeployed within the area by computing the number of total vehicles usinga formula: total vehicles=number of all ports in the area times numberof predicted ride demands per average travel time between ports, whereinthe number of predicted ride demands per average travel time betweenports is tabulated on daily basis.

Furthermore, these and other objects of the invention are fulfilled byproviding a vehicle distribution system for distributing vehicles amonga plurality of ports within an area in response to ride demandsgenerated at each of said ports, said vehicle distribution systemcomprising: predicted ride demand data storing means for storingpredicted ride demand data established on the basis of statistical ridedemand data regarding all ports; vehicle count detecting means fordetecting an existing vehicle count at each of said ports; demanddetecting means for detecting ride demand information including acurrent ride demand count and destination information regarding each ofsaid ports; arriving vehicle predicting means for predicting arrivals ofvehicles at each port from other ports as a predicted arriving vehiclecount based on said destination information; surplus and shortagecomputing means for computing either a surplus or a shortage of vehiclesat each of said ports by comparing, within a range of search representedby a predetermined period of time at each port, said current ride demandcount and said predicted ride demand data, with said existing vehiclecount and said predicted arriving vehicle count; and vehicleredistributing means for redistributing vehicles from a port havingsurplus vehicles to a port lacking vehicles on the basis of results ofthe computation indicating either said surplus or said shortage ofvehicles.

Other objects and further scope of applicability of the presentinvention will become apparent from the detailed description givenhereinafter. However, it should be understood that the detaileddescription and specific examples, while indicating preferredembodiments of the invention, are given by way of illustration only,since various changes and modifications within the spirit and scope ofthe invention will become apparent to those skilled in the art from thisdetailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention will become more fully understood from thedetailed description given hereinbelow and the accompanying drawingswhich are given by way of illustration only, and thus, are notlimitative of the present invention, and wherein:

FIG. 1 is a system diagram illustrating a vehicle allocation system, inaccordance the present invention;

FIG. 2 is a block diagram illustrating principal functions of a terminalat each port and a host computer;

FIG. 3 is a table illustrating passenger demands and numbers of vehiclesin all ports before any reallocation of the vehicles;

FIG. 4 is a table illustrating passenger demands and numbers of vehiclesin all ports after reallocation of the vehicles;

FIG. 5 is a flowchart of an algorithm for determining shortages andexcesses of vehicles at the ports;

FIG. 6 is a flowchart of an algorithm for accomplishing reallocation ofthe vehicles based upon the results of flowchart in FIG. 5;

FIG. 7 is a table illustrating an example of travel time requirementsfor vehicles to move between ports;

FIG. 8 is a graph illustrating a relationship between the number ofassigned vehicles and the number of reallocation vehicles;

FIG. 9 is a graph illustrating a relationship between the number ofassigned vehicles and the average waiting time;

FIGS. 10(a) and 10(b) are tables illustrating vehicle running timesbetween ports;

FIG. 10(c) is a table illustrating frequencies of trips between specificports in percentages;

FIG. 11 is a graph illustrating relations between the number of deployedvehicles, the average waiting time and the number of reallocationvehicles;

FIG. 12 is a graph illustrating changes in passenger demands in a day;

FIG. 13 is a graph illustrating a relation between a product of thenumber of reallocated vehicles, a deployed vehicle count, the averagewaiting time, and the SD time using a search ranges as a parameter.

FIG. 14 is a graphic representation of demands that occurred in the pastas opposed to demands currently generated;

FIG. 15 is a table indicating how vehicles are typically redistributedon the basis of raw statistical data;

FIG. 16 is a graphic representation showing how probabilities of demandoccurrence typically vary with respect to statistical data;

FIG. 17 is a table indicating how vehicles are typically redistributedin accordance with the probability of demand occurrence;

FIG. 18 is a graphic representation showing typical ratios of startingtrips per destination;

FIG. 19 is a graphic representation showing arriving trips predictedthrough multiplication of probabilities of demand occurrence bydestination ratios;

FIG. 20 is a block diagram showing key functions for redistributingvehicles based on the probability of demand occurrence and ondestination ratios;

FIG. 21 is a graph illustrating deviations of waiting times per deployedvehicle count;

FIG. 22 is a graph also illustrating deviations of waiting times perdeployed vehicle count;

FIG. 23 is a graph illustrating a relation between the waiting time andthe number of deployed vehicles using the accommodating capacity as aparameter;

FIG. 24 is a flowchart of steps constituting a modification of thevehicle redistribution process; and

FIG. 25 is a graph illustrating a relation between the waiting time andthe number of deployed vehicles within the area, with the accommodatingcapacity of each port being used as a parameter.

DETAILED DESCRIPTION OF THE INVENTION

Preferred embodiments of this invention will now be described in detailwith reference to the accompanying drawings.

FIG. 1 is a schematic diagram illustrating a typical vehicledistribution system embodying the invention. In this example, five portsare assumed to exist within an area. Ports PI through P5 (genericallycalled the port P hereunder where appropriate) represent parking spacesat such places as a golf course, an airport, a hotel, etc. A pluralityof vehicles 4 is deployed within the area. An optimum number of vehiclesto be operated in the area will be discussed later. Each port P has aterminal 2. Each terminal 2 is furnished with a sensor 3 that detectsthe comings and goings of the vehicles 4.

The sensor 3 is capable of identifying a vehicle 4 by detecting itsvehicle number. The vehicle number to be detected may be that of anumber plate attached to the front and/or rear of each vehicle, or somesuitable number provided on the side or top of each vehicle fordetection. The vehicle number is not limited to numerals. It may beformed by identification information using a bar code, characters, marksand/or other symbols. The sensor 3 may be an optical, electrical ormagnetic sensor for optically or electronically reading such vehiclenumbers from vehicles.

Each terminal 2 has an identification unit (not shown) for identifyingvehicle users. The identification unit checks an ID number, or otherdata, entered by a vehicle user to see if the user is a registeredcontractor, e.g. a vehicle user known to the system for billingpurposes. The ID number or like data should be stored preferably on anIC card. The identification unit reads data from the IC card submittedby a user prior to vehicle use. When the use of the vehicle isterminated, the user again submits his or her IC card to theidentification unit, which upon reading the IC card, verifies the end ofvehicle use. The terminal 2 also comprises an input unit (not shown)through which users enter desired destinations. The input unit may beformed by a set of switches corresponding to the port names.

Each vehicle 4 may illustratively be an electric vehicle that isauto-piloted. When a user is allowed to use a vehicle, the doors of thevehicle are unlocked and the vehicle is made ready to be started.Instead of having its doors automatically unlocked, the vehicle may beunlocked manually by a user utilizing his or her IC card. In any case,it is preferred that the identification information (ID number, etc.)identifying each potential vehicle user carrying an IC card berecognized by the terminal 2 of the port P before the user rides avehicle.

The terminals 2 are connected to a host computer 1 (called the hosthereunder) through communication lines. Data are exchanged between theterminals 2 and the host 1. The terminal 2 of a port P, at which avehicle user wishes to ride a vehicle, transmits to the host 1 thevehicle numbers of the existing vehicles and the existing vehicle countat the port P in question, as well as the ID number of the contractorand the ride demand. A demand occurs when a user enters his or her IDnumber. Each demand includes destination information. Given such an IDnumber, the host 1 references stored personal information on contractorsto decide whether to allow the use of a vehicle for the user inquestion.

Following a decision in favor of the vehicle use, the host 1 allows theterminal 2 to rent a vehicle and to designate a specific vehicle to berented. The permission to rent a vehicle and the designation of thevehicle to be rented permit the user to actually ride the vehicle. Therent permission and the vehicle designation give rise to what is calleda “starting trip.”

The terminal 2 of a port P at which a vehicle user riding a vehicle hasarrived transmits to the host 1 the vehicle numbers of currentlyavailable vehicles and the existing vehicle count at the port P, thecontractor ID number of the user which arrived, recognized arrivalinformation (called an arriving trip), and travel data about thecontractor. An arriving trip is output when the sensor 3 detects thearrival at the port P of the vehicle 4 corresponding to the startingtrip.

The host 1 has computing means (CPU) 10 and a storage device (memory)11. The CPU 10, in conjunction with the memory 11, performs computationsto supply the terminal 2 with permission to rent vehicles anddesignation of vehicles to be rented on the basis of information enteredthrough the terminal 2. The host 1 also includes a communication device12 for giving instructions to each vehicle 4.

The memory 11 stores predicted ride demand data (called starting tripshereunder) about each port P as part of statistical ride demand dataregarding all ports. Also stored in the memory 11 are contractorinformation and contractor travel data. Predicted starting tripsrepresent predicted daily demands based on past demand results.Contractor information denotes personal information such as contractornames associated with ID numbers. Contractor travel data are made up ofcontractors's ride distances and ride times used as accountinginformation to be used when the contractors are later billed.

FIG. 2 is a block diagram showing key functions of a terminal 2 and thehost 1. The terminal 2 includes a demand reporting unit 20, an arrivingtrip reporting unit 21, a contractor ID reporting unit 22 and anexisting vehicle count reporting unit 23 for reporting respectivelydemands, arriving trips, contractor IDs and an existing vehicle count tothe host. The demand reporting-unit 20 reports to the host 1 theoccurrence of a demand whenever an ID number entered by a user isrecognized. The arriving trip reporting unit 21 notifies the host 1 ofarrivals of vehicles detected by the sensor 3. The contractor IDreporting unit 22 informs the host 1 of ID numbers read from IC cards orother medium. The existing vehicle count reporting unit 23 reports thecurrent number of vehicles counted on the basis of the vehicle numbers,as well as, comings and goings of vehicles detected by the sensor 3.

The terminal 2 also has a display unit 24 that instructs or guides usersto ride vehicles. In giving instructions or guidance to users, thedisplay unit 24 relies on permission to rent vehicles or other suitabledirectives from the host 1. The instructions or guidance may be given aseither visual or audio information. The terminal 2 comprises acommunication interface 25 for exchanging data with the host 1. If avehicle is currently available at the port P, and if the display unit 24is capable of issuing an available vehicle instruction immediately afterthe input of an ID code, the display unit 24 may indicate the applicablevehicle number. If no vehicle is currently available at the port P, thedisplay unit 24 may indicate a predicted waiting time.

The memory 11 of the host 1 has a predicted starting trip storing unit110 and a contractor information storing unit 111. The predictedstarting trip storing unit 110 accumulates daily demand results at eachport in the form of time series data, and supplies the CPU 10 with thedata denoting predicted starting trips. Presented as predicted startingtrips, the past demand results may preferably be stored in accordancewith the weather patterns, the time zones of the day, the days of theweek, and other suitable criteria. This will allow the CPU 10 to getdata on predicted starting trips in a specific time zone on a specificday of the week under a specific weather pattern. The contractorinformation storing unit 111 stores contractor information including thecontractors's travel data. The contractor information is storedbeforehand, and the contractors's travel data are entered from theterminals 2.

The CPU 10 is connected via a communication interface 107 to thecommunication interfaces 25 of the terminals 2. A demand countdetermining unit 101 of the CPU 10 determines a predicted number ofvehicles demanded per hour on the basis of the predicted starting tripsheld in the memory 11. Depending on whether the predicted number ofdemanded vehicles exceeds a reference count, a search depth (SD) timedetermining unit 102 determines a search range, i.e., a search depth(SD) time which spans predetermined hours (or minutes) starting from thepresent time and in which to search for predicted starting trips. Analgorithm for determining the SD time will be described later.

A predicted ride demand detecting unit 103 reads from the predictedstarting trip storing unit 110 predicted starting trips within the SDtime determined by the search depth time determining unit 102, andoutputs what is read to a surplus/shortage computing unit 104. Thesurplus/shortage computing unit 104 computes a surplus or a shortage ofvehicles based on the demands and existing vehicle count sent from theterminal 2 at each port P, as well as on the above-mentioned predictedstarting trips. The computation of the vehicle surplus or shortage takesinto account those arriving trips at destination ports which arepredicted by the destination information included in the demands.

On the basis of the surplus or shortage of vehicles 4 at each port P, avehicle redistribution determining unit 105 outputs instructions formoving excess vehicles 4 from one port P to another, i.e., forredistributing vehicles 4. Vehicle redistribution instructions arereported to the vehicles 4 via the communication device 12. Each vehicle4 has a communication device and an automatic traveling unit allowingthe vehicle to respond to redistribution instructions. The automatictraveling unit may be a position detecting system utilizing map data andGPS (global positioning system) data, or a known system relying ontraffic signals and an obstruction monitoring and avoidance scheme.

If there is any available vehicle, a vehicle distribution determiningunit 106 immediately notifies the applicable terminal 2 of permission torent and designation of the vehicle to be rented. If a vehicle islacking, the vehicle distribution determining unit 106 computes awaiting time based on a predicted arriving time of a redistributedvehicle designated by the vehicle redistribution determining unit 105.The terminal 2 is notified of the waiting time which is presented to thevehicle user waiting for a vehicle.

How vehicles are distributed will now be illustratively described. Ifvehicles were distributed solely on the basis of the existing vehiclecount and current demands at each port P, efficient distribution ofvehicles would be impossible because of fluctuating demands and constantmovements of vehicles resulting in an additional surplus or shortage ofvehicles. This bottleneck is circumvented by redistributing vehicleswhile considering demands and arriving trips within a predetermined SDtime. FIG. 3 is a schematic diagram showing how the number of vehiclesvaries at each port P in keeping with the starting and arriving trips atthe ports. This diagram takes into account those trips that arepredicted to occur in the current SD time, but does not considervehicles to be redistributed.

In FIG. 3, the port P1 has a demand count of 3 and an existing vehiclecount of 0. That is, the port P1 lacks three vehicles at present. Theport P1 is subject to two arriving trips: an arriving trip Ta1 stemmingfrom a starting trip that occurred earlier at another port, and anarriving trip Ta11 resulting from a starting trip Td3 that occurred atthe port P3 at the start of the current SD time. Furthermore, a startingtrip Tf1 is predicted to occur. This brings the total demand count to 4.Since two vehicles are available in the current SD time against thedemand count of 4, two vehicles are lacking at the port P1.

The port P2 has a demand count of 0 and an existing vehicle count of 5.That is, the port P2 has five surplus vehicles. With starting trips Tf2and Tf21 predicted to occur at the port P2, the total demand count isbrought to 2. Because five vehicles are available in the current SD timeagainst the demand count of 2 at the port P2, three vehicles are insurplus there.

The port P3 has a demand count of 5 and an existing vehicle count of 2.This means that the part P3 currently lacks three vehicles. With twovehicles currently available, the port P3 immediately meets two demandscausing starting trips Td3 and Td31 to occur. The port P3 is subject toarriving trips Ta3 and Ta31 stemming from starting trips that occurredat other ports in a previous SD time. With no predicted starting trip,the total demand count remains at 5. Because four vehicles are availablein the current SD time against the demand count of 5, the port P3 lacksone vehicle.

The port P4 has a demand count of 1 and an existing vehicle count of 1and thus has no surplus or shortage of vehicles at present Because ithas one vehicle currently available, the port P4 immediately meets theexisting demand, causing a starting trip Td4 to occur. With a startingtrip Tf4 predicted to occur at the port P4, its total demand count isbrought to 2. Furthermore, an arriving trip Ta4 is expected to occur dueto a starting trip originated at the port P3. That is, two vehicles areavailable in the current SD time against the demand count of 2, so thatthere is no surplus or shortage of vehicles at the port P4.

The port P5 has a demand count of 0 and an existing vehicle count of 1and thus has one vehicle currently in surplus. The port P5 is subject totwo arriving trips: an arriving trip Ta5 stemming from the starting tripTd4 originated at the port P4, and an arriving trip Ta51 derived from astarting trip originated at another port in a previous SD time. With astarting trip Tf5 predicted to occur at the port, its total demand countamounts to 1. When the demand count is 1 is compared against the threevehicles available in the current SD time, the port P5 has two surplusvehicles.

Vehicles are redistributed on the assumption that vehicle and demandcounts vary in the SD time. Below is a description of a multistagealgorithm for vehicle redistribution. In a first stage of the algorithm,the ports with vehicles that may be redistributed within the SD time andthe number of these available vehicles are detected. In the aboveexample, the ports P2 and P5 have surplus vehicles that may beredistributed. In a second stage, the remaining number of vehiclesfollowing the redistribution of the surplus vehicles is obtained. In athird stage, a check is made to see if the remaining vehicles are enoughto met the demands that may occur next.

Illustratively, a demand cannot be met immediately if it occurs at agiven port P as a result of an arriving trip after all vehicles havebeen redistributed and before an available vehicle count at the port inquestion is replenished. In such a case, vehicle redistribution isdeemed feasible if the remaining vehicle count is judged enough to coverthe newly generated demand.

In the example above, when the port P2 has three surplus vehiclesredistributed to other ports, it is still left with two vehicles insurplus. Even after meeting the predicted starting trip Tf2 occurringnext, the port P2 has one vehicle in surplus. Thus the port P2 has threevehicles that may be redistributed, and the redistribution is feasible.Meanwhile, when the port P5 has two surplus vehicles redistributed, itis left with one vehicle. After meeting the demand of the predictedstarting trip Tf5 occurring next, the port P5 has no surplus vehicleleft so that vehicle redistribution is deemed unfeasible.

Preferably, vehicles should be redistributed to ports P short ofvehicles from the nearest ports P. If the port P5 is not subject to anynew starting demand, then both the port P2 and the port P5 may havetheir vehicles redistributed. Vehicles are redistributed from whicheverport is the closest to any vehicle-lacking port. It is assumed here thatthe ports P1 and P3 short of vehicles are closer to the port P2 than tothe port P5. On that assumption, two vehicles are moved from the port P2to the port P1 and one vehicle from the port P2 to the port P3.

FIG. 4 is a schematic diagram depicting how the number of vehiclesvaries at each port P in the SD time following vehicle redistributionbased on the rearrangements above. In FIG. 4, the first userrepresenting the current demand at the port P1 may ride a vehiclecorresponding to the arriving trip Ta1. The second and the third usersmay ride two vehicles (Dv1, Dv2) redistributed from the port P2. Theuser representing the predicted starting trip Tf1 may ride a vehicleprovided by the arriving trip Ta11.

The port P2 has two vehicles (d1, d2) redistributed to the port P1 andone vehicle (d3) moved to the port P3. The user corresponding to thepredicted starting trip Tf2 may ride a currently available vehicle (V1),and the user representing the predicted starting trip Tf21 may rideanother currently available vehicle (V2).

The port P3 with its currently available two vehicles can immediatelymeet two of its five demands. That is, the first and the second usersmay ride vehicles represented by the starting trips Td3 and Td31. Thethird and the fourth users may ride vehicles provided by the arrivingtrips Ta3 and Ta31. The fifth user may ride a vehicle (Dv3)redistributed from the port P2.

The port P4 with its currently available one vehicle can immediatelymeet one demand. That is, the user may ride a vehicle of the startingtrip Td4. Another user corresponding to a predicted starting trip Tf4may ride a vehicle of the arriving trip Ta4. The port P4 is shown herereceiving an arriving trip Ta41, which stems from the predicted startingtrip Tf5 originated at the port T5 and which was not taken intoconsideration for vehicle redistribution because it was not predictableat the port P4.

The port P5 has one vehicle available but is subject to no demand. Thismeans that a starting trip will not occur immediately. The usercorresponding to the predicted starting trip Tf5 may ride the existingvehicle V5. Vehicles of the subsequent arriving trips Ta5 and Ta51remain undistributed. The port P5 is shown getting an arriving tripTa52, which stems from the predicted starting trip Tf4 originated at theport P4 and which was not taken into consideration for vehicleredistribution because it was not predictable at the port P5.Alternatively, the arriving trip Ta52 may be taken into account forvehicle redistribution based on statistical data.

As a result of the vehicle redistribution above, the ports P1 through P3meet all demands in the SD time without surplus or shortage of vehicles.The port P4 has one excess vehicle left and the port P5 has threevehicles left in surplus. In the above example, all demands have beenmet in the current SD time. If there are any vehicles for which thedemands were not met within the SD time in question, they will becarried over to the vehicle redistribution process in the next SD time.If a maximum waiting time is determined beforehand, and if that maximumwaiting time is exceeded in the current SD time, demands are met byredistributing available vehicles, including the vehicles at the portsP, that were determined earlier not to be subject to vehicleredistribution.

The above processing of vehicle redistribution will now be describedwith reference to a flowchart. FIG. 5 is a flowchart of steps forcomputing any surplus or shortage of vehicles being distributed. In stepS1, of FIG. 5, a parameter P (representing a given port) is set equal to0. In step S2, the parameter P is incremented by 1. The steps thatfollow concern the port P represented by the parameter P, e.g. P1, P2.

In step S3, the number of carried-over arriving trips, i.e., the numberof arriving trips based on the starting trips that had occurred up tothe preceding computation process, is set to a parameter NTA. In stepS4, an existing vehicle count is set to the parameter NPV. In step S5,an existing demand count is set to a parameter DP. In step S6, apredicted starting trip count is set to a parameter DT.

In step S7, a check is made to see if any arriving trips occur in thecurrent SD time. The check is based on computations verifying whetherany starting trips occur at any other ports, whether the destinationinformation included a demand that the starting trip include the oneport, and whether any of such arriving trips will reach the one portwithin the current SD time. The computations take into account the knowndistances between the ports and the expected travel speeds of thevehicles involved.

If any arriving trips are detected in step S7, step S8 is reached. Instep S8, the number of arriving trips (NTA′) is added to the arrivingtrip count NTA as well as the existing vehicle count NPV. The sumdenotes an available vehicle count NP. In step S9, a surplus or shortageof vehicles is computed. Specifically, the existing demand count DP andthe predicted starting trip count DT are subtracted from the availablevehicle count NP in order to acquire a surplus/shortage of vehicles.

In step S10, a check is made to see if the number of available vehiclesfollowing vehicle redistribution is sufficient. The decision of step S10is made on the basis of whether there are any vehicles left afterredistribution of the vehicles judged to be in surplus upon computationof the surplus/shortage of vehicles and whether these remaining vehiclesare enough to meet demands that may subsequently occur. If the result ofthe check in step S10 is affirmative, step S11 is reached.

In step S11, a flag PF is set to indicate that vehicle redistribution isfeasible. If vehicle redistribution is deemed unfeasible, step S12 isreached and the flag PF is cleared. In step S13, a check is made to seeif the parameter P has reached 5, i.e. whether the surplus or shortageof vehicles has been computed for all the ports. If the result of thecheck in step S13 is negative, step S2 is reached again, and thecomputing process is repeated until the parameter P is found to havereached 5, covering all of ports P1 through P5.

Now, with reference to the flowchart of FIG. 6, a description of howvehicles are redistributed on the basis of the computation of a surplusor a shortage of vehicles will be given. In step S20, the parameter P(representing a given port P) is set equal to 0. In step S21, theparameter P is incremented by 1. In step S22, a check is made to see ifthe flag PF is set indicating the presence of available vehicles at theport P for redistribution. If the flag PF shows vehicle redistributionto be feasible, step S23 is reached. In step S23, a check is made to seeif any port within a predetermined distance from the port P lacksvehicles. The check of step S23 is intended to make sure that anyavailable vehicles are redistributed preferentially to the nearest portwithin the predetermined minimum distance from each port having surplusvehicles.

If the result of the check in step S23 is affirmative, step S24 isreached. In step S24, vehicles are redistributed from the port P inquestion to other ports short of vehicles. There may be cases, however,in which vehicles are in fact unavailable at the moment despite thevehicles being counted as available within the SD time. Thus only thosevehicles that are currently available will be redistributed. Theredistribution of vehicles is followed by step S25. In step S25, thenumber of surplus or lacking vehicles at each port is changed to reflectthe altered number of vehicles following the redistribution.

If the result of the check in either step S22 or step S23 is negative,i.e. if the port in question has no vehicles that may be redistributedor if there is no port short of vehicles within the predetermineddistance of the port, then step S41 is reached.

In step S41, a check is made to see if the number of accommodatedvehicles CAP is at most the predicted number of vehicles NP available inthe SD time. If the result of the check in step S41 is affirmative,i.e., if more vehicles than can be accommodated are expected to arriveat the port within the SD time, then step S42 is reached. In step S42, acheck is made to see if there are any other ports short of vehicles. Ifsuch a port is detected, step S24 is reached in which vehicles areredistributed to that port.

If there is no port short of vehicles, step S42 is followed by step S43.In step S43, the port currently having the fewest vehicles is detected.When the port with the smallest number of vehicles is determined, stepS24 is reached in which vehicles are redistributed to that port. Afterthe vehicle redistribution, step S25 is reached in which the number ofsurplus or lacking vehicles at each of the ports is updated using theirlatest vehicle counts.

If steps S41 through S43 have led to a judgment that the one port isincapable of accommodating the existing number of vehicles plus thepredicted number of arriving vehicles, vehicles are redistributed to avehicle lacking port that may not be located in the vicinity. Suchredistribution prevents congestion of vehicles at the one port due toits lack of accommodating space. If there is no port short of vehicles,excess vehicles are redistributed to the port with the smallest numberof existing vehicles. This again suppresses congestion at the one port.

In step S26, the parameter P is checked to see if all ports have beenprocessed. If the result of the check in step S26 is affirmative, stepS27 is reached. In step S27, a check is made to see if there still existports short of vehicles. If no port is found to be short of -vehicles instep S27, the processing is terminated. If any port is found to lackvehicles, step S28 is reached. In step S28, a check is made to see if apredetermined maximum waiting time is exceeded in which no vehicles areto be redistributed within the current SD time. If the waiting time isnot exceeded, step S29 is reached. In step S29, the lacking vehicles arecarried over as demands into the next SD time. That is, these vehiclespersist as existing demands in the next process of computing the surplusor shortage of vehicles.

If the maximum waiting time is exceeded, a search is made for surplusvehicles at more distant ports. Specifically, a longer distance isestablished in step S30 to expand the range of ports subject to thesearch. With the applicable distance thus extended, a remote port may befound to possess surplus vehicles, but an attempt to redistributevehicles from that port may be judged to exceed the maximum waitingtime. Given that possibility, in step S31, a check is made to see if themaximum waiting time is exceeded in an attempt to resolve the shortageof vehicles through vehicle redistribution from any port within thenewly established distance.

If the maximum waiting time is found to be exceeded in step S31, theattempt to redistribute vehicles from faraway ports is abandoned andstep S29 is reached. In step S29, arrangements are made so that theshortage of vehicles will be replenished in the next SD time. If themaximum waiting time is not found to be exceeded in step S31, step S20is reached again. In step S23 following step S30, a check is made to seeif any port within the newly extended distance from the port P lacksvehicles.

To further explain steps S41 through S43 executed above, if the sum ofthe existing vehicle count and the predicted arriving vehicle count isjudged to exceed the accommodating capacity of the one port, thenvehicles are redistributed to a vehicle-lacking port that may not belocated in the vicinity, whereby congestion at the one port is averted.If no port is found to lack vehicles, vehicles are redistributed to theport currently having the fewest vehicles so as to avoid congestion atthe one port. If a plurality of ports are each found to have a smallnumber of vehicles, vehicles may alternatively be redistributed to thenearest of these ports.

Below is a description of an algorithm for setting the SD time. FIG. 7is a schematic diagram showing typical time periods required toredistribute vehicles among the ports involved. As shown in FIG. 7, ittakes a maximum of 30 minutes to redistribute a vehicle from one port tothe farthest port, and a minimum of 5 minutes from one port to thenearest port. As evident from FIG. 7, a number of vehicles determined tobe redistributed in an SD time of less than five minutes will not reachtheir destination ports within that SD time.

In an SD time of at least five minutes and less than seven minutes,vehicles may be redistributed only between the ports P1 and P2. In an SDtime of at least seven minutes and less than nine minute, vehicles maybe redistributed only between the ports P1 and P2 and between the portsP2 and P3. Examining the SD time frame in this manner reveals thatvehicles may be redistributed among all ports in an SD time of at least30 minutes. As described, to redistribute vehicles requires establishingan SD time equivalent to at least a minimum period of time needed tomove vehicles between ports P. In the example of FIG. 7, the SD time isat least five minutes.

The SD time should be shorter than the maximum waiting time for avehicle user. Given a maximum waiting time of 15 minutes, the SD timeshould be set for less than 15 minutes. In that case, vehicles may beredistributed between the ports P1 and P2; between the ports P2 and P3;between the ports P3 and P4; and between the ports P4 and P5. Ifvehicles are allowed to travel automatically for redistribution, thetime it takes to travel between ports is obviously determined by thevelocity of such automated movement.

Described below is an algorithm for determining the SD time inconnection with the number of vehicles deployed. Where as many vehiclesas the total number of demands are allocated to a given port P, there isobviously no need to redistribute any vehicles to that port P. Thesmaller the number of vehicles allocated to a port, the greater thenumber of vehicles to be redistributed to that port. It follows that ifa large number of vehicles are deployed to meet only a limited need forredistributing vehicles, the SD time tends to be shorter and the waitingtime at each port P is more likely to be reduced.

Since deployment of an unlimited number of vehicles is not economical,it is desirable to reduce the vehicle count by prolonging the SD timeand by making good use of the vehicle redistribution process. Aninordinately long SD time coupled with a small number of deployedvehicles can prolong waiting time. Although longer SD time periods canstretch waiting time, an increasing number of arriving trips areexpected from other ports so that the number of redistributed vehiclesbecomes relatively small. In any case, an optimum SD time should bedetermined through an overall trade-off between the number of deployedvehicles, the number of redistributed vehicles, and the waiting time.

FIG. 8 is a graphic representation illustrating relations between thenumber of deployed vehicles and the number of redistributed vehiclesusing the SD time as a parameter, and FIG. 9 is a graphic representationdepicting relations between the number of deployed vehicles and theaverage waiting time also using the SD time as a parameter. In FIG. 8,on the assumption that the number of deployed vehicles is at most “a”and that the number of redistributed vehicles is at most “A,” reducingthe deployed vehicle count lowers the redistributed vehicle count in thesame SD time (assuming SD1>SD2>SD3>SD4).

On the other hand, reducing the deployed vehicle count prolongs theaverage waiting time, as shown in FIG. 9. That is, the number ofredistributed vehicles declines when the number of deployed vehicles isreduced, which results in a prolonged waiting time.

It follows that to keep the average waiting time from exceeding itsupper limit B requires increasing the number of redistributed vehicles.This in turn necessitates shortening the SD time. In other words, toreduce the redistributed vehicle count requires prolonging the SD time;to minimize the average waiting time requires shortening the SD time.

If points L, M and H are established illustratively as shown in FIGS. 8and 9, both the upper limit B of the average waiting time and themaximum redistributed vehicle count A are satisfied at each of thepoints. Thus any one of the three factors, i.e., deployed vehicle count,redistributed vehicle count and average waiting time, may be selected toreceive priority in accordance with what is specifically needed at agiven point in time (e.g., by a business-related decision).

What follows is a description of how an optimum number of vehicles canbe deployed at the ports, according to the invention. If it is desiredto reduce the waiting time to zero despite differences between demandsthat actually occur at each port on the one hand and the demands deducedfrom predicted starting trips based on statistical ride demand data onthe other hand, each port need only have one vehicle in theory everytime a single demand has occurred.

Meanwhile, if a demand has occurred at a given port and if there areenough vehicles deployed so that a vehicle may be redistributed from theport of the demanded destination to the port having generated thatdemand, then the two ports are always furnished with available vehiclesupon elapse of the time required for the vehicle movement between thetwo ports. That is, the number of vehicles deployed at all ports isagain brought to the initial status.

Suppose that the vehicle travel time is constant between all ports, thata demand has occurred at a given port, and that a vehicle isredistributed from any one of the other ports to the port havinggenerated the demand. In such a case, if vehicles are redistributedbetween ports to replenish their vehicle counts as described above, thenall ports acquire vehicles upon elapse of the vehicle travel time. Ifonly one demand occurs in each vehicle travel time between ports, thendeploying one vehicle at each port will reduce the waiting timetheoretically to zero.

It follows that, in practice, the number of all deployed vehicles may bedetermined by finding out the number of predicted starting tripsoccurring in each vehicle travel time within the entire area, on thebasis of the number of all predicted starting trips per day.

An example in which specific numbers are simulated will now bedescribed. FIGS. 10A and 10B are schematic diagrams showing travel timesamong the ports P1 through P5, whereby area sizes are determined. FIG.10A lists typical travel times in effect when users drive vehicles (at48 km/h), and FIG. 10B gives typical travel times in effect whenvehicles travel unattended (at 16 km/h). In this example, a total of 75vehicles are deployed, with each port being assigned 15 vehicles. Themaximum average waiting time is set for one minute because thissimulation example gives priority to the waiting time.

FIG. 11 is a graphic representation depicting relations between thenumber of deployed vehicles, the waiting time, and the number ofredistributed vehicles under the simulation conditions mentioned above.The SD time is set for 20 minutes. Where the maximum average waitingtime is set for one minute in FIG. 11, the number of vehicles needed tobe deployed is 75, and the number of vehicles redistributed under theseconditions is 473.

The SD time need not be fixed for the whole day and may be varieddepending on the predicted ride demands. FIG. 12 is a graphicrepresentation showing actual ride demands on a typical day. The day'stotal ride demands amount to 1,800 trips. As illustrated in FIG. 12, theride demands typically fluctuate considerably throughout the day. Whenthere are many ride demands, the SD time should preferably be shortenedin order to maximize the number of redistributed vehicles therebyminimizing prolongation of the waiting time.

Illustratively, one-half of the maximum predicted ride demands per daymay be selected as a reference level. When the predicted ride demandsfall below the reference level, the SD time may be set for 20 minutes;when the predicted ride demands are on or above the reference level, theSD time may be shortened 10 to 15 minutes. If the maximum predicted ridedemands are assumed to be at 180 vehicles in FIG. 12, the referencelevel for altering the SD time is set for 90 vehicles. Thus the SD timeis set for 20 minutes in time zones T1, T3 and T5; and for 15 minutes intime zones T2 and T4.

An optimum number of vehicles to be deployed is now calculated under theabove conditions of simulation. The travel time for automated vehicleruns is regarded as a reference travel time between ports. This isbecause the travel time for redistributing vehicles is necessarilylonger due to unattended vehicle runs than when vehicles are driven byusers. A simple average of the vehicle travel times listed in FIG. 10Bis 14.46 minutes (about 15 min.). If the number of all predictedstarting trips per day is 1,800, then approximately 18 starting tripsoccur every 15 minutes. This means that about 18 trips take place whileboth manned and unattended vehicles come and go between ports toreplenish their vehicle counts.

Thus, each port need only be provided with as many vehicles as thenumber of predicted starting trips per average vehicle travel timebetween ports. More specifically, the five ports are to have 18 vehicleseach, i.e. a total of 90 vehicles deployed in the area. Under thisscheme, each starting trip is responded to with a vehicle redistributedfrom one of the ports. In theory, each port always has one availablevehicle when a demand occurs.

In practice, there are a number of variables to take into account. Thetravel time varies between ports; the total number of starting tripsoccurring per day varies; demands can concentrate in certain time zones.These variables combine to prevent the waiting time from always beingzero. Still, when the system is capable of redistributing vehicles bypredicting starting and arriving trips based on statistical data asdescribed above, fluctuations in the waiting time can be minimizeddespite deviations of actual demands from predicted values.

FIGS. 21 and 22 are graphic representations illustrating deviations ofwaiting times per deployed vehicle count in the area, includingdeviations of actual demands (starting trips) from predicted startingtrips deduced from statistical ride demand data. In FIGS. 21 and 22, theaxis of the abscissa represents the number of trips divided by thenumber of ports. FIG. 22 is an enlarged view of the waiting timedeviations in FIG. 21.

As shown in FIGS. 21 and 22, where 90 vehicles are deployed, thedeviations of waiting times are small and stable, regardless of themagnitude of differences between the statistical data and the actualdemands. Where the deployed vehicle count is between 72 and 54, thedeviations of waiting times are quite pronounced and fluctuateconsiderably, if the actual demands deviate from the statistical data.If the ratio of the number of trips to the number of ports becomessmall, i.e. if the number of trips is very small compared with thenumber of ports, the waiting time deviations are enormous. If the numberof trips is 1,800 compared with the port count being 10 or less, thedeviations of waiting times in connection with larger than the number ofvehicles traveling between the ports P1 and P5.

One such example is shown in FIG. 10C. FIG. 10C illustrates frequenciesof trips between specific ports in percentages reflecting differenttravel times therebetween. In this case, it is preferable to obtain nota simple average but a weighted average of the travel times between theports. From FIGS. 10B and 10C, the average travel time is computed to be11.91 minutes (about 12 min.). That is, a total of 1,800 trips over 20hours translate into 18 trips approximately every 12 minutes. Thus, thefive ports are to be furnished with a total of 90 vehicles.

Such different frequencies of trips between specific ports are unique toa given area. Wherever an area is set aside for the vehicle distributionsystem, the varying frequencies of trips may be inferred from specificfeatures of the area or may be determined by collecting relevant dataabout the area.

It is thus preferable to consider different frequencies of trips betweenports as well as irregular distribution of demands throughout the day indetermining a vehicle's travel time between ports, as well as, thenumber of predicted starting trips within the travel time. The vehicletravel time between ports may be dealt with not as a simple average, butas a weighted average of varying travel times between ports, theaveraging being weighted by taking different frequencies of port-to-porttrips taken into account. In such a case, the duration of the day neednot be limited to 24 hours.

Other criteria for setting the SD time will now be described. FIG. 13 isa graphic representation showing simulated relations between a productof the redistributed vehicle count and the average waiting time (theproduct is called the distribution coefficient hereunder) on the onehand, and the SD time on the other hand, using the number of deployedvehicles as a parameter. The data in FIG. 13 apply when vehicles beingredistributed travel at 35 km/h. Each SD time period is indicated interms of the ratio of the SD time in question with respect to the traveltime (20 minutes) between the farthest of the multiple ports configured(the ratio is called the SD ratio hereunder). The smaller thedistribution coefficient, the more efficient the system becomes. Thatis, because shorter waiting times signify fewer occasions on whichvehicles are moved empty of riders.

As indicated in FIG. 13, the distribution coefficient is conspicuouslyminimal when the number of deployed vehicles is below a certain level.Specifically, in areas where the number of deployed vehicles is lessthan 60 and where the SD ratio is between 1 and 1.5 or thereabout, thedistribution coefficient reaches its minimum level. In areas where thenumber of deployed vehicles is 60 or higher, the distributioncoefficient is not conspicuously minimal. That is, the distributioncoefficient changes little with regard to SD ratio fluctuations. Inother words, in areas where a large number of vehicles are deployed,establishing the SD time according to strict criteria does notnecessarily yield expected good results because of the leeway providedby the sufficient number of vehicles.

If a slightly insufficient number of vehicles are deployed to meetdemands, selecting a suitable SD time makes it possible to construct aneconomical system with a low distribution coefficient. In the example ofFIG. 13, an efficient system is constituted if, with fewer than 60vehicles deployed, the SD ratio is set between 1 and 1.5.

In areas where the number of deployed vehicles is less than 45, theaverage waiting time is at least 10 minutes because there are too fewvehicles to meet demands. In the latter case, the distributioncoefficient reaches its minimum level when the SD ratio is 1 orthereabout.

The embodiment described above does not take into account the number ofvehicles that may be accommodated at each port, i.e., the capacity of aparking lot of each port. If the number of vehicles that may be parkedat each port (called the accommodating capacity hereunder) is smallwhile the total number of deployed vehicles within the area isconsiderable, congestion can occur when vehicles are coming in and goingout. This can result in a prolonged waiting time despite the largenumber or vehicles deployed.

FIG. 23 is a graphic representation showing relations between thewaiting time and the number of vehicles using the accommodating capacityas a parameter. As indicated in FIG. 23, there are certain vehiclecounts at which the waiting time is minimal, and increasing the numberof vehicles does not necessarily shorten the waiting time.Illustratively, if the number of all deployed vehicles is 75, thewaiting time is about 4 minutes for the accommodating capacity (CAP) of30 vehicles, 2 minutes for the capacity of 40 vehicles, and 1 minute forthe capacity of 50. If the number of accommodated vehicles were 20, thewaiting time would be too long to be shown in the figure.

In view of the difficulty above, a second embodiment described below isdesigned to redistribute vehicles by taking the accommodating capacityof each port into consideration. FIG. 24 is a flowchart of stepsconstituting another process of vehicle redistribution, a modificationof the vehicle redistribution process in FIG. 6. Of the step numbers inFIG. 24, those already used in FIG. 6 designate like or correspondingsteps.

In FIG. 24, if the result of the check in step S22 or S23 is negative,i.e. if the port in question has no vehicles that may be redistributedor if no port within the predetermined short distance lacks vehicles,then step S41 is reached. In step S41, a check is made to see if theaccommodating capacity CAP is at most the number of vehicles NP that maybe used in the SD time.

If the result of the check in step S41 is affirmative, i.e. if a numberof vehicles greater than the accommodating capacity of the port inquestion are predicted to arrive within the SD time, then step S42 isreached. In step S42, a check is made to see if any other port lacksvehicles. If any port is found to be short of vehicles, step S24 isreached, in which vehicles are redistributed to the port in question.

If there are no ports short of vehicles, step S42 is followed by stepS43. In step S43, the port currently having the fewest vehicles isdetected. When the port with the smallest existing vehicle count isidentified, step S24 is reached. In step S24, vehicles are redistributedto the port in question. After vehicle redistribution, step S25 isreached. In step S25, the number of excess or lacking vehicles at eachport is updated to reflect the latest vehicle counts. Step S26 isfollowed by the same process as that of steps S27 through S31 in FIG. 6;the process is omitted from FIG. 15 and will not be described further.

If the sum of the existing vehicle count and the predicted arrivingvehicle count is judged to exceed the accommodating capacity of the oneport, vehicles are redistributed to a vehicle-lacking port that may notbe located in the vicinity, whereby congestion at the one port isaverted. If no port is found to lack vehicles, vehicles areredistributed to the port currently having the fewest vehicles so as toavoid congestion at the one port. If a plurality of ports are each foundto have a small number of vehicles, vehicles may alternatively beredistributed to the nearest of these ports.

FIG. 25 is a graphic representation showing relations between thewaiting time with accommodating capacity taken into account and thenumber of vehicles deployed within the area, the accommodating capacityof each port being used as a parameter. As depicted in FIG. 25, thelarger the number of deployed vehicles, the shorter the waiting time atall ports regardless of the accommodating capacity thereof. Inparticular, even where the accommodating capacity is as small as 20vehicles, the waiting time is at most 4 minutes provided the number ofvehicles is at least 75.

As described, where vehicles are redistributed with the accommodatingcapacity at each port taken into consideration, the waiting time can bereduced in keeping with the number of deployed vehicles. The deployedvehicle count is determined by taking into account both the waiting timeand the number of vehicles to be redistributed. Unlike the case of FIG.23 in which a growing number of deployed vehicles causes the waitingtime to start getting longer at some point, the setup of FIG. 25 allowsthe number of deployed vehicles to be determined within a wide rangeillustratively based on business decisions.

The embodiment described above predicts starting trips based on pastride demands (statistical data) stored in the predicted starting tripstoring unit 110, and redistributes vehicles according to thepredictions thus acquired. However, actually generated demands candeviate from raw statistical data, and appreciable degrees of suchvariance can lead to wasteful redistribution of vehicles.

Described below is an example of a vehicle redistribution process causedby a discrepancy between statistical ride demand data and actualdemands. FIG. 14 is a graphic representation of demands that occurred inone hour in the past as opposed to demands currently generated. FIG. 15is a schematic diagram indicating how vehicles are redistributed at oneport on the basis of the data in FIG. 14. In both FIGS. 14 and 15,demands for two vehicles have already occurred at a judging time TA, andpast demands allow a starting trip of one vehicle to be predicted,occurring from the time TA into the SD time. If one vehicle is assumedto be currently available, then one of the three demands is met, andredistribution instructions for the other two vehicles are issued.

By a judging time TB, the vehicles designated for redistribution at thepreceding judging time TA have been redistributed, bringing the vehiclecount to 3. With two vehicles allocated to meet the two demands, theexisting vehicle count reaches 1. After the two demands have beenprocessed, another four demands occur. From the judging time TB into theSD time, predicted starting trips of six vehicles occur. Because onevehicle is currently available against the demands for 10 vehicles,redistribution instructions for nine vehicles are issued. If it isassumed that the requested nine vehicles arrive not between the judgingtime TB and a judging time TC, but past the judging time TC, only onevehicle currently exists as opposed to four demands. The unprocesseddemands are carried over past the judging time TC.

At the judging time TC, three unprocessed demands are supplemented bytwo more demands, bringing the existing demand count to 5. Because thereare predicted starting trips of six vehicles, the existing vehicle countof 9 is two vehicles short. This again requires issuing vehicleredistribution instructions for the missing vehicles.

In the same manner, checks are made at subsequent judging times TD andTE to see if vehicles need to be -redistributed. As shown in thefigures, unlike in the preceding judgments, surplus vehicles aredetected at the times TD and TE for redistribution to other ports. Thatis, in one hour of vehicle redistribution, four excess vehicles need tobe redistributed.

The wasteful redistribution of vehicles illustrated above stems from thedifference between statistical ride demand data and actual demands. Thelack of measures to correlate the two kinds of data is one major reasonfor the defect. Given such assessment, another embodiment of theinvention, to be described below, uses the statistical data not as rawdata but as mean data per unit time and associates meaningfully thestatistical data with actual demands for vehicle redistribution. Amongothers, the statistical data are grasped as probabilities of demand(starting trip) occurrence so as to reduce discrepancies between thestatistical data and the actual demands.

FIG. 16 is a graphic representation showing how probabilities of demandoccurrence typically vary. Based on the prediction that demands for 10vehicles will occur per hour, the probability of demands occurring perminute may be regarded as 10/60 (1/6) vehicles. The probability isreduced whenever demands are actually generated. That is, in keepingwith the demands shown occurring in the upper part of FIG. 16, theprobability of demand occurrence is reduced progressively to 8/60, 4/60,2/60 and 1/60.

The predicted starting trips mentioned above are modified by taking intoaccount the probability of demand occurrence. FIG. 17 is a schematicdiagram indicating how vehicles are typically redistributed whilepredicted starting trips are modified in accordance with the probabilityof demand occurrence. At a judging time TA, the probability is 8/60(2/15) because two demands have occurred so far. The value representsthe number of vehicles per minute, so that demands for 2.7 vehicles(rounded to 3 vehicles) are predicted for the SD time (20 minutes inthis case). As a result, redistribution instructions for four vehiclesare issued at the judging time TA.

At a judging time TB, four vehicles are “redistributed and one demand ismet so that the existing vehicle count is 3. Because demands for fourmore vehicles have occurred by the time TB, the probability of demandoccurrence is 4/60 (1/15). As a result, demands for 1.3 vehicles(rounded to 2 vehicles) are predicted for the SD time and redistributioninstructions for three vehicles are issued at the judging time To. Theredistributed vehicle count in this case is smaller by six than thevehicle count at the time TB in FIG. 15.

In like manner, demands are predicted for vehicle redistribution inaccordance with the modified probability of demand occurrence. Thisimplements a vehicle redistribution scheme with a minimum of wastefullyredistributed vehicles. Illustratively, no vehicle is redistributed at ajudging time TD and only one vehicle is redistributed at a judging timeTE.

Statistical ride demand data may include ratios of demands based onpredicted starting trips with respect to destination ports. That is, thestatistical data may comprise data regarding which port becomes adestination port at what ratio. FIG. 18 is a graphic representationshowing ratios of predicted demand counts at a port P1 with regard todestination ports. As illustrated in FIG. 18, 80 percent of thedestinations are set to a port P2 with the first demand in a givenone-hour period; 60 percent are set to the same port P2 with the nextdemand in the same period; and so on. For a plurality of demandsoccurring at the same time, FIG. 18 indicates an average ratio.

FIG. 19 is a schematic view showing probabilities of demand occurrencejuxtaposed with probabilities of demands whose destination is the portP2. In FIG. 19, each probability of demand occurrence is expressed interms of the number of predicted starting trips, i.e., the number ofpredicted ride demands, in the SD time. As depicted, until a firstdemand actually occurs, arriving trips of 10/3×0.8 vehicles arepredicted to occur at the port P2 in the SD time. Whenever a demand isactually generated, the predicted arriving trips are modified by takingthe demand ratio above into account. Every time a demand occurs at theport Pi, the predicted arriving trip is reported to the port P2. At theport P2, the predicted arriving trip is added to the prediction datawhen the number of vehicles to be redistributed is determined. Predictedarriving trips are reported between ports.

FIG. 20 is a block diagram showing key functions of the terminal 2 atthe port PI for redistributing vehicles based on the probability ofstarting trip (demand) occurrence and on the predicted arriving trips. Apredicted starting trip counting unit 201 (i.e., ride demand predictingmeans) computes the number of predicted starting trips in the SD timebased on the predicted starting trips “A” per unit time and on theaccumulated demands “x” having actually occurred per unit time. Theformula for the computation is shown in FIG. 20. The predicted startingtrips “A” may be determined according to the statistical data about pastride demands. The computation is carried out at the beginning of eachunit time and every time a demand has actually occurred.

A demand counting unit 202 (i.e., ride demand count detecting means)adds up the predicted starting trip count and the number of currentlygenerated demands to compute the number of demands at the port PI in theSD time. A vehicle counting unit 203 adds up the existing vehicle countand the number of arriving trips predicted to occur in the SD time, tocompute the number of vehicles available in the SD time. The arrivingtrips are of two types: arriving trips deduced from the starting tripsthat occurred at other ports before the computation, and predictedarriving trips reported from other ports.

A predicted arriving trip counting unit 204 multiplies the number ofpredicted starting trips computed by the predicted starting tripcounting unit 201, by the destination ratio of the predicted startingtrips, in order to compute the number of predicted arriving trips toother ports. The predicted arriving trip count thus computed is reportedto each of the other ports.

At ports other than the port P1, the number of predicted arriving tripsto each port is computed and transmitted. The number of predictedarriving trips to the port P1, received from the other ports, is inputto the vehicle counting unit 203 for use in computing the number ofvehicles.

A surplus/shortage computing unit 205 computes the difference betweenthe demand count and the vehicle count to detect a surplus or a shortageof vehicles. A vehicle redistribution determining unit 206 determinesthe number of actually redistributed vehicles by taking into account thesurplus or shortage of vehicles at the other ports. As described earlierwith reference to FIG. 1, the surplus/shortage computing unit 205 andvehicle redistribution determining unit 206 should preferably befurnished as functions of the host 1.

The above-described embodiment redistributes vehicles more efficientlythan before by regarding statistical data as probabilities of demandoccurrence in the SD time, and not by predicting the trip count in theSD time based on raw statistical data. Furthermore, the computedprobability is reduced progressively in keeping with the actuallygenerated trips, which ensures that vehicles are redistributed moreefficiently than ever.

The number of vehicles made available in the SD time can also bepredicted accurately in accordance with the predicted arriving tripscomputed on the basis of which of the ports applicable to predictedstarting trips probably serves as the destination. Illustratively, themeasures above make it possible to predict the arriving trips Ta41 andTa52 in FIG. 4.

Although the above-described embodiments have been shown envisaging asystem in which the vehicles 4 are run automatically for redistribution,this is not limitative of the invention. Alternatively, vehicles 4 maybe redistributed by human drivers or may be towed by tractor or someother appropriate vehicle for the purpose. The invention applies notonly to the system of distributing vehicles that are driven by users butalso to a distribution system for taxis and limousines.

As described, this invention provides a vehicle distribution systemcapable of redistributing vehicles based on predicted ride demand dataduring a vehicle distribution process. If actual ride demands deviatefrom the predicted ride demand data, the system offers a high degree ofleeway in redistributing vehicles and thereby minimizes fluctuations ofvehicle waiting times at each of the ports configured in the area.

As described, the vehicle distribution system according to the inventionpredicts vehicle ride demands and the number of vehicles in apredetermined range of search. Unlike the conventional system thatleaves to human judgments the extent to which the predicted ride demandsare to be taken into consideration for vehicle distribution, theinventive system ensures stable distribution of vehicles by eliminatingarbitrary instructions for vehicle distribution.

The invention being thus described, it will be obvious that the same maybe varied in many ways. Such variations are not to be regarded as adeparture from the spirit and scope of the invention, and all suchmodifications as would be obvious to one skilled in the art are intendedto be included within the scope of the following claims.

We claim:
 1. A method of operating a vehicle allocation system forallocating a first number of vehicles amongst a second number of vehicleports where passengers demand the services of one or more vehicles, saidmethod comprising the steps of: acquiring past vehicle demand data basedupon past passenger transportation activity; forming predictive vehicledemand data, based upon the past vehicle demand data; storing thepredictive vehicle demand data; detecting a number of available vehiclesat each of the ports; sensing current vehicle demand data representingpassengers actually seeking transportation at each of the ports; readingvehicle destination and arrival data of vehicles starting or in transitto one of the ports; predicting the number of arriving vehicles at eachof the ports based upon the vehicle destination and arrival data;determining whether a given port has a deficiency or an excess ofvehicles by analyzing the current vehicle demand data, the predictivevehicle demand data, the number of available vehicles, and the number ofarriving vehicles, for a predetermined period of time; and reallocatingvehicles from a port determined to have an excess of vehicles to a portdetermined to have a deficiency of vehicles.
 2. The method according toclaim 1, further including the step of: setting the predetermined periodof time to be more than a minimum travel time required for moving avehicle between ports during said reallocating step.
 3. The methodaccording to claim 1, wherein a plurality of predetermined times areavailable and one of the predetermined times is selected based upon anumber of all vehicles deployed and a number of vehicles to bedistributed, so that a predetermined number of vehicles to bereallocated will not be exceeded.
 4. The method according to claim 1,wherein a plurality of predetermined times are available and one of thepredetermined times is selected based upon a number of all vehiclesdeployed and an average waiting time of passengers, so that apredetermined maximum waiting time for passengers will not be exceeded.5. The method according to claim 1, wherein said predetermined timevaries over a course of a day.
 6. The method according to claim 1,wherein said predetermined time varies dependent upon a day of the week.7. The method according to claim 1, wherein said predetermined timevaries in response to weather conditions.
 8. The method according toclaim 5, wherein said predetermined time is shortened if the predictivevehicle demand data exceeds a predetermined level during the course ofthe day.
 9. The method according to claim 1, wherein the product of thenumber of vehicles reallocated in said reallocating step and an averagewaiting time for passengers is obtained by simulation with thepredetermined time used as a parameter, and selecting a predeterminedtime such that the product is minimized.
 10. The method according toclaim 1, wherein if a total number of vehicles deployed is less than apredetermined count, the predetermined time is set approximately equalto a maximum time required to reallocate vehicles between ports.
 11. Themethod according to claim 1, wherein if a number of parking spaces at agiven port is approximately equal to or less than a sum of the availablevehicles at the given port and a predicted number of arriving vehiclesat the given port, a vehicle or vehicles at the given port arereallocated from the given port during said reallocating step regardlessof whether an excess of vehicles at the given port is determined. 12.The method according to claim 11, wherein the vehicle or vehicles arereallocated to a port having the least available vehicles.
 13. A vehicledistribution system for distributing vehicles among a plurality of portswithin an area in response to ride demands generated at each of saidports, said vehicle distribution system comprising: predicted ridedemand data storing means for storing predicted ride demand dataestablished on the basis of statistical ride demand data regarding allports; vehicle count detecting means for detecting an existing vehiclecount at each of said ports; demand detecting means for detecting ridedemand information including a current ride demand count and destinationinformation regarding each of said ports; arriving vehicle predictingmeans for predicting arrivals of vehicles at each port from other portsas a predicted arriving vehicle count based on said destinationinformation; surplus and shortage computing means for computing either asurplus or a shortage of vehicles at each of said ports by comparing,within a range of search represented by a predetermined period of timeat each port, said current ride demand count and said predicted ridedemand data, with said existing vehicle count and said predictedarriving vehicle count; and vehicle redistributing means forredistributing vehicles from a port having surplus vehicles to a portlacking vehicles on the basis of results of the computation indicatingeither said surplus or said shortage of vehicles.
 14. The vehicledistribution system according to claim 13, wherein said range of searchrepresented by said predetermined period of time is set approximatelyequal to, or greater than, a minimum travel time required to movevehicles between ports for redistribution.
 15. The vehicle distributionsystem according to claim 13, wherein said range of search representedby said predetermined period of time varies in the course of a day. 16.The vehicle distribution system according to claim 15, wherein saidrange of search represented by said predetermined period of time isshortened if said predicted ride demand data exceeds a predeterminedlevel during the day.